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Journal ArticleDOI

Estimating the impact of school closure on influenza transmission from Sentinel data

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TLDR
It is shown that holidays lead to a 20–29% reduction in the rate at which influenza is transmitted to children, but that they have no detectable effect on the contact patterns of adults, as well as predicting the effect of school closure during a pandemic.
Abstract
The threat posed by the highly pathogenic H5N1 influenza virus requires public health authorities to prepare for a human pandemic. Although pre-pandemic vaccines and antiviral drugs might significantly reduce illness rates, their stockpiling is too expensive to be practical for many countries. Consequently, alternative control strategies, based on non-pharmaceutical interventions, are a potentially attractive policy option. School closure is the measure most often considered. The high social and economic costs of closing schools for months make it an expensive and therefore controversial policy, and the current absence of quantitative data on the role of schools during influenza epidemics means there is little consensus on the probable effectiveness of school closure in reducing the impact of a pandemic. Here, from the joint analysis of surveillance data and holiday timing in France, we quantify the role of schools in influenza epidemics and predict the effect of school closure during a pandemic. We show that holidays lead to a 20-29% reduction in the rate at which influenza is transmitted to children, but that they have no detectable effect on the contact patterns of adults. Holidays prevent 16-18% of seasonal influenza cases (18-21% in children). By extrapolation, we find that prolonged school closure during a pandemic might reduce the cumulative number of cases by 13-17% (18-23% in children) and peak attack rates by up to 39-45% (47-52% in children). The impact of school closure would be reduced if it proved difficult to maintain low contact rates among children for a prolonged period.

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Journal ArticleDOI

Remaining Motivated despite the Limitations: University Students' Learning Propensity during the COVID-19 Pandemic.

TL;DR: The importance of preparing students to be more resilient and to enable them to cultivate the ability to remain optimistic and motivated to succeed and overcome any of life’s adversities is recommended.
Journal ArticleDOI

Modeling the effects of influenza vaccination of health care workers in hospital departments

TL;DR: Most scenarios under study show a similar or higher impact of hospital HCW vaccination than has been predicted for the long-term care nursing home department, and it seems justified to extend the recommendations forHCW vaccination, based on results in the long,term care setting, to short- term care settings as well.
Journal ArticleDOI

Disease dynamics in wild populations: modeling and estimation: a review

TL;DR: The need to account for detectability in modeling disease dynamics is demonstrated, and a number of mark–recapture and occupancy study designs for estimating disease parameters while simultaneously accounting for variation in detectability are explored.
Journal ArticleDOI

Genealogy with seasonality, the basic reproduction number, and the influenza pandemic.

TL;DR: It is shown that R0 for periodic models is still an asymptotic per generation growth rate, and the difference between this theoretical R0For periodic models and the “reproduction number” obtained by fitting an exponential to the beginning of an epidemic curve is emphasized.
References
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BookDOI

Markov Chain Monte Carlo in Practice

TL;DR: The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.
BookDOI

Sequential Monte Carlo methods in practice

TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Book

Monte Carlo strategies in scientific computing

Jun Liu
TL;DR: This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared.
Journal ArticleDOI

Strategies for mitigating an influenza pandemic

TL;DR: It is found that border restrictions and/or internal travel restrictions are unlikely to delay spread by more than 2–3 weeks unless more than 99% effective, and vaccine stockpiled in advance of a pandemic could significantly reduce attack rates even if of low efficacy.
Journal ArticleDOI

Monte Carlo Strategies in Scientific Computing

Tim Hesterberg
- 01 Nov 2002 - 
TL;DR: The strength of this book is in bringing together advanced Monte Carlo methods developed in many disciplines, including the Ising model, molecular structure simulation, bioinformatics, target tracking, hypothesis testing for astronomical observations, Bayesian inference of multilevel models, missing-data problems.
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